{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取客户流失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>account_length</th>\n",
       "      <th>area_code</th>\n",
       "      <th>international_plan</th>\n",
       "      <th>voice_mail_plan</th>\n",
       "      <th>number_vmail_messages</th>\n",
       "      <th>total_day_minutes</th>\n",
       "      <th>total_day_calls</th>\n",
       "      <th>total_day_charge</th>\n",
       "      <th>total_eve_minutes</th>\n",
       "      <th>total_eve_calls</th>\n",
       "      <th>total_eve_charge</th>\n",
       "      <th>total_night_minutes</th>\n",
       "      <th>total_night_calls</th>\n",
       "      <th>total_night_charge</th>\n",
       "      <th>total_intl_minutes</th>\n",
       "      <th>total_intl_calls</th>\n",
       "      <th>total_intl_charge</th>\n",
       "      <th>number_customer_service_calls</th>\n",
       "      <th>churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KS</td>\n",
       "      <td>128</td>\n",
       "      <td>area_code_415</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>25</td>\n",
       "      <td>265.1</td>\n",
       "      <td>110</td>\n",
       "      <td>45.07</td>\n",
       "      <td>197.4</td>\n",
       "      <td>99</td>\n",
       "      <td>16.78</td>\n",
       "      <td>244.7</td>\n",
       "      <td>91</td>\n",
       "      <td>11.01</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.70</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OH</td>\n",
       "      <td>107</td>\n",
       "      <td>area_code_415</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>26</td>\n",
       "      <td>161.6</td>\n",
       "      <td>123</td>\n",
       "      <td>27.47</td>\n",
       "      <td>195.5</td>\n",
       "      <td>103</td>\n",
       "      <td>16.62</td>\n",
       "      <td>254.4</td>\n",
       "      <td>103</td>\n",
       "      <td>11.45</td>\n",
       "      <td>13.7</td>\n",
       "      <td>3</td>\n",
       "      <td>3.70</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NJ</td>\n",
       "      <td>137</td>\n",
       "      <td>area_code_415</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>243.4</td>\n",
       "      <td>114</td>\n",
       "      <td>41.38</td>\n",
       "      <td>121.2</td>\n",
       "      <td>110</td>\n",
       "      <td>10.30</td>\n",
       "      <td>162.6</td>\n",
       "      <td>104</td>\n",
       "      <td>7.32</td>\n",
       "      <td>12.2</td>\n",
       "      <td>5</td>\n",
       "      <td>3.29</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OH</td>\n",
       "      <td>84</td>\n",
       "      <td>area_code_408</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>299.4</td>\n",
       "      <td>71</td>\n",
       "      <td>50.90</td>\n",
       "      <td>61.9</td>\n",
       "      <td>88</td>\n",
       "      <td>5.26</td>\n",
       "      <td>196.9</td>\n",
       "      <td>89</td>\n",
       "      <td>8.86</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>OK</td>\n",
       "      <td>75</td>\n",
       "      <td>area_code_415</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>166.7</td>\n",
       "      <td>113</td>\n",
       "      <td>28.34</td>\n",
       "      <td>148.3</td>\n",
       "      <td>122</td>\n",
       "      <td>12.61</td>\n",
       "      <td>186.9</td>\n",
       "      <td>121</td>\n",
       "      <td>8.41</td>\n",
       "      <td>10.1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.73</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  account_length      area_code international_plan voice_mail_plan  \\\n",
       "1    KS             128  area_code_415                 no             yes   \n",
       "2    OH             107  area_code_415                 no             yes   \n",
       "3    NJ             137  area_code_415                 no              no   \n",
       "4    OH              84  area_code_408                yes              no   \n",
       "5    OK              75  area_code_415                yes              no   \n",
       "\n",
       "   number_vmail_messages  total_day_minutes  total_day_calls  \\\n",
       "1                     25              265.1              110   \n",
       "2                     26              161.6              123   \n",
       "3                      0              243.4              114   \n",
       "4                      0              299.4               71   \n",
       "5                      0              166.7              113   \n",
       "\n",
       "   total_day_charge  total_eve_minutes  total_eve_calls  total_eve_charge  \\\n",
       "1             45.07              197.4               99             16.78   \n",
       "2             27.47              195.5              103             16.62   \n",
       "3             41.38              121.2              110             10.30   \n",
       "4             50.90               61.9               88              5.26   \n",
       "5             28.34              148.3              122             12.61   \n",
       "\n",
       "   total_night_minutes  total_night_calls  total_night_charge  \\\n",
       "1                244.7                 91               11.01   \n",
       "2                254.4                103               11.45   \n",
       "3                162.6                104                7.32   \n",
       "4                196.9                 89                8.86   \n",
       "5                186.9                121                8.41   \n",
       "\n",
       "   total_intl_minutes  total_intl_calls  total_intl_charge  \\\n",
       "1                10.0                 3               2.70   \n",
       "2                13.7                 3               3.70   \n",
       "3                12.2                 5               3.29   \n",
       "4                 6.6                 7               1.78   \n",
       "5                10.1                 3               2.73   \n",
       "\n",
       "   number_customer_service_calls churn  \n",
       "1                              1    no  \n",
       "2                              1    no  \n",
       "3                              0    no  \n",
       "4                              2    no  \n",
       "5                              3    no  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('Data/customer_churn.csv', index_col=0, header = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据前处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.ix[:,3:]\n",
    "cat_var = ['international_plan','voice_mail_plan', 'churn']\n",
    "\n",
    "for var in cat_var:\n",
    "    df[var] = df[var].map(lambda e: 1 if e == 'yes' else 0)\n",
    "y = df.ix[:,-1]\n",
    "X = df.ix[:,:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#y.shape\n",
    "#X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 区分训练与测试数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_X, test_X, train_y, test_y = train_test_split(X, y, test_size = 0.33, random_state = 123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1100, 16)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.shape\n",
    "test_X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用决策树建立分类模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=3,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "\n",
    "clf = tree.DecisionTreeClassifier(max_depth=3)\n",
    "clf.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted  = clf.predict(test_X)\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89363636363636367"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy\n",
    "numpy.sum(test_y == predicted) / len(test_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 产生Confusion Matrix 以及其它评估指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.89363636363636367"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[924,  26],\n",
       "       [ 91,  59]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "m = confusion_matrix(test_y, predicted)\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.91      0.97      0.94       950\n",
      "          1       0.69      0.39      0.50       150\n",
      "\n",
      "avg / total       0.88      0.89      0.88      1100\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(test_y, predicted))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用ROC Curve 比较模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf1 = DecisionTreeClassifier()\n",
    "clf1.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=7000, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=10000, probability=True, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "clf2 = SVC(probability=True, kernel='linear', cache_size=7000, max_iter=10000)\n",
    "clf2.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "clf3 = LogisticRegression()\n",
    "clf3.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=10, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf4 = RandomForestClassifier()\n",
    "clf4.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:220: ConvergenceWarning: Solver terminated early (max_iter=10000).  Consider pre-processing your data with StandardScaler or MinMaxScaler.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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v7NZ/d5K9oiIVxu2aMye9ypvVkZTZuZTbnbTuvXVYv3l9emdh69Qw/Oe3KbQJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9ab14a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "plt.figure(figsize= [20,10])\n",
    "for clf, title in zip([clf1,clf2,clf3, clf4], ['Decision Tree', 'SVM', 'LogisticRegression', 'RandomForest']):\n",
    "    probas_ = clf.fit(train_X, train_y).predict_proba(test_X)\n",
    "    fpr, tpr, thresholds = roc_curve(test_y, probas_[:, 1])\n",
    "    plt.plot(fpr, tpr, label='%s - AUC:%.2f'%(title, auc(fpr, tpr)) )\n",
    "    \n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate', fontsize = 20)\n",
    "plt.ylabel('True Positive Rate', fontsize = 20)\n",
    "plt.title('Receiver operating characteristic example', fontsize = 20)\n",
    "plt.legend(loc=\"lower right\", fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 4, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [2,5,4,1,3]\n",
    "b = numpy.array(a)\n",
    "b.argsort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_day_minutes', 'total_day_charge',\n",
       "       'number_customer_service_calls', 'international_plan',\n",
       "       'total_eve_charge', 'total_eve_minutes', 'total_intl_calls',\n",
       "       'total_night_minutes', 'total_night_charge', 'total_intl_charge',\n",
       "       'number_vmail_messages', 'total_day_calls', 'total_eve_calls',\n",
       "       'total_intl_minutes', 'total_night_calls', 'voice_mail_plan'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clf1.feature_importances_.argsort()\n",
    "#train_X.columns\n",
    "#clf1.feature_importances_.argsort()[::-1]\n",
    "train_X.columns[clf4.feature_importances_.argsort()[::-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估特征重要性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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y8O9VbxwRb254v5OZX/xAs/VTxjOr6+s10Dh5vQ5YShqS2BJ4E9BkjDdnm3I/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\nM7MW+P+wADwjjQD54QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc700160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "importance = clf4.feature_importances_\n",
    "names = train_X.columns\n",
    "plt.title('Feature Importance')\n",
    "plt.bar(range(0, len(names)), importance[importance.argsort()[::-1]])\n",
    "plt.xticks(range(0,len(names)), names[importance.argsort()[::-1]], rotation=90)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
